In silico methods for predicting drug–drug interactions with cytochrome P-450s, transporters and beyond

Ni, Ai; Fan, Xiaohui; Ekins, Sean

doi:10.1016/j.addr.2015.03.006

Cited by 37 publications

(17 citation statements)

References 243 publications

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“…Furthermore, toxicity can also be caused by drug–drug interaction (DDI) which can lead to the withdrawal of drugs from the market1399. Several types of DDI can occur and various in silico drug-drug interaction prediction engines have been developed88100101. For instance, in silico DDI assessments can be performed by estimating the possible binding of a compound to important proteins that participate in DDI such as CYP enzymes102 and transporters (e.g, P-gp)93103.…”

Section: Resultsmentioning

confidence: 99%

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

Lagorce¹,

Douguet²,

Miteva³

et al. 2017

Sci Rep

166

116

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The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

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Section: Resultsmentioning

confidence: 99%

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

Lagorce¹,

Douguet²,

Miteva³

et al. 2017

Sci Rep

166

116

View full text Add to dashboard Cite

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“…For patients taking these comedications, higher doses of GW406381 would be required . Further drug–drug interaction studies would be required to establish the requirement for dose adjustment in these subgroups …”

Section: Discussionmentioning

confidence: 99%

Biomarker exposure–response relationships as the basis for rational dose selection: Lessons from a simulation exercise using a selective COX‐2 inhibitor

Taneja

Oosterholt

Danhof

et al. 2015

The Journal of Clinical Pharma

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An exposure-response model was used to characterize the pharmacokinetic-pharmacodynamic relationship of GW406381, a COX-2 inhibitor, based on data from ex vivo prostaglandin E2 inhibition collected in a phase 1 study in healthy subjects. The final model was then used to simulate a proof-of-concept study and to explore suitable dosing ranges in case of hepatic dysfunction or metabolic induction. Trough concentrations in the range of IC80 to IC95 were used as target therapeutic concentrations. Symptom relief in a subsequent phase 2b study in 400 patients with rheumatoid arthritis receiving GW406381 was then analysed to support the design of a phase 3 study in which doses in the range between 10 to 400 mg were explored. The exercise also allowed the evaluation of correlations between the biomarker and clinical end point. A 2-compartment model described the pharmacokinetics of GW406381, whereas the pharmacodynamics was described by an Imax model. In patients with normal organ function, the predicted median therapeutic dose was between 100 and 150 mg. The time course of symptom relief was fitted by a Weibull model, with an Imax model describing the drug effect. Simulations showed that dose-response discrimination occurred at doses higher than 150 mg, with predicted 60%-80% target engagement in the dose range of 150-400 mg.

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“…DDIs arise when one of the concomitant drugs modulates the metabolism of co-administered drug by induction or inhibition of CYP enzymes involved in its metabolism, which may bring about detrimental consequences such as alteration of the concentration of drug in the blood, pharmacological activity and/or adverse drug reactions. [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of 16-dehydropregnenolone on the activity and expression of rat hepatic cytochrome P450 enzymes

Ramakrishna

Bhateria

Singh

et al. 2016

The Journal of Steroid Biochemistry and Molecular Biology

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In silico methods for predicting drug–drug interactions with cytochrome P-450s, transporters and beyond

Cited by 37 publications

References 243 publications

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

Biomarker exposure–response relationships as the basis for rational dose selection: Lessons from a simulation exercise using a selective COX‐2 inhibitor

Evaluation of the impact of 16-dehydropregnenolone on the activity and expression of rat hepatic cytochrome P450 enzymes

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